U.S. patent number 9,759,659 [Application Number 13/044,787] was granted by the patent office on 2017-09-12 for method for detecting the impacts of interfering effects on experimental data.
This patent grant is currently assigned to EVOTEC AG. The grantee listed for this patent is Christian Eggeling, Nicolas Fay, Maciej Hoffman-Wecker, Pierre Ilouga, Kaupo Palo. Invention is credited to Christian Eggeling, Nicolas Fay, Maciej Hoffman-Wecker, Pierre Ilouga, Kaupo Palo.
United States Patent |
9,759,659 |
Eggeling , et al. |
September 12, 2017 |
Method for detecting the impacts of interfering effects on
experimental data
Abstract
A method for identifying the impact on data, such as
experimental data, of interfering effects, such as unwanted
auto-fluorescence, fluorescence quenching, and fluorescent-sample
deterioration, whether or not the data fulfill certain criteria
with respect to a threshold indicative of the interfering
effects.
Inventors: |
Eggeling; Christian (Hamburg,
DE), Palo; Kaupo (Harjumaa, EE), Fay;
Nicolas (Hamburg, DE), Hoffman-Wecker; Maciej
(Hamburg, DE), Ilouga; Pierre (Hamburg,
DE) |
Applicant: |
Name |
City |
State |
Country |
Type |
Eggeling; Christian
Palo; Kaupo
Fay; Nicolas
Hoffman-Wecker; Maciej
Ilouga; Pierre |
Hamburg
Harjumaa
Hamburg
Hamburg
Hamburg |
N/A
N/A
N/A
N/A
N/A |
DE
EE
DE
DE
DE |
|
|
Assignee: |
EVOTEC AG (Hamburg,
DE)
|
Family
ID: |
32926408 |
Appl.
No.: |
13/044,787 |
Filed: |
March 10, 2011 |
Prior Publication Data
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Document
Identifier |
Publication Date |
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US 20110218767 A1 |
Sep 8, 2011 |
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Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
Issue Date |
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12656444 |
Jan 29, 2010 |
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12068644 |
Feb 8, 2008 |
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10378081 |
Mar 4, 2003 |
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60361288 |
Mar 4, 2002 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G01N
21/6445 (20130101); G01N 2201/1215 (20130101) |
Current International
Class: |
G06F
17/18 (20060101); G01N 21/64 (20060101) |
Field of
Search: |
;702/19,21,22,23,26,27,28,29,30,32,35,36,40,57,81,82,128,13,7,179,180,181,182,189-194
;250/305-311,580-582,370.01,370.08,390.07,395,550-552,559.01-559.08
;356/36-38,300,302-303,450,451,213,218,237.1 |
References Cited
[Referenced By]
U.S. Patent Documents
Foreign Patent Documents
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2 318 636 |
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Apr 1998 |
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GB |
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2 318 636 |
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Apr 1998 |
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GB |
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WO 01/01112 |
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Jan 2001 |
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WO |
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WO 01/01112 |
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Jan 2001 |
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WO |
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01/07896 |
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Feb 2001 |
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WO |
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WO 01/07896 |
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Feb 2001 |
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WO |
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Other References
Kask et al. "Two Dimensional Fluorescence Intensity Distribution
Analysis: Theory and Applications". Biophysical Journal, 78:4
(2000), 1703-13. cited by applicant .
XP-002250629, 2001. cited by applicant .
Danzer et al. "Chemometrik" 2001, Springer, XP 8.4.2. cited by
applicant.
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Primary Examiner: Ngon; Ricky
Attorney, Agent or Firm: Jacobson Holman PLLC
Parent Case Text
This is a continuation of Ser. No. 12/656,444, filed, Jan. 29,
2010, now abandoned which is a divisional of Ser. No. 12/068,644,
filed, Feb. 8, 2008, now abandoned which is a continuation of Ser.
No. 10/378,081, filed, Mar. 4, 2003, now abandoned which claims the
benefit of U.S. Provisional Application No. 60/361,288, filed Mar.
4, 2002.
Claims
The invention claimed is:
1. A system for detecting the impact of and correcting for
interfering effects of auto-fluorescence, fluorescence quenching,
and fluorescence-signal deterioration on fluorescence emission data
resulting from fluorescence measurements, said system comprising:
(i) a device for supporting one or a plurality of samples selected
from the group consisting of drug-candidate samples and
patient-specimen samples in an inspection station, (ii) one or a
plurality of fluorescence signal detectors which are positioned
relative to the inspection station so that a fluorescence signal
emitted from the samples impinges on the fluorescence signal
detectors, and (iii) a fluorescence signal processing unit
programmed to perform the steps of receiving fluorescence emission
data generated by the fluorescence signal detectors, determining
values of a plurality of fluorescence identification parameters
from said fluorescence emission data including at least first and
second corresponding fluorescence identification parameters,
storing the determined values in such a manner that all the
determined values which relate to any one of the samples are
linked, creating a multi-dimensional histogram or distribution of
the determined values of the fluorescence identification
parameters, determining thresholds for the determined values of the
fluorescence identification parameters from said multi-dimensional
histogram or distribution, which thresholds are indicative of
interfering effects selected from the group consisting of
auto-fluorescence, fluorescence quenching, and fluorescence-signal
deterioration, wherein the threshold for the determined values of
the first fluorescence identification parameter is a function of
the corresponding second fluorescence identification parameter,
analyzing the determined values of the plurality of fluorescence
identification parameters whether or not the determined values
fulfill one or a plurality of criteria with respect to the
thresholds, supplying as output information fluorescence emission
data influenced by the interfering effects, fluorescence emission
data not affected by the interfering effects, or fluorescence
emission data influenced by the interfering effects and
fluorescence emission data not affected by the interfering effects,
and using the output information to detect at least one of false
positive drug-candidate test results in the fluorescence emission
data, false negative drug-candidate test results in the
fluorescence emission data, false positive diagnostic test results
in the fluorescence emission data, and false negative diagnostic
test results in the fluorescence emission data.
2. The system of claim 1 further comprising a fluorescence
reader.
3. The system of claim 1 further comprising a fluorescence reader
including a confocal optical set-up.
4. The system of claim 1 wherein the photosensitive detector
comprises an avalanche photodiode or a charged coupled device (CCD)
camera.
Description
This invention relates to a method for detecting the impacts of
interfering effects on experimental data such as secondary light
emission data. More particularly, the invention relates to a method
for detecting impacts of the effects of unwanted auto-fluorescence,
of fluorescence quenching, and/or of general deterioration of the
light signal on the measured data.
In the rapidly evolving field of nano-biotechnology, manipulation
of particles and objects are important issues. Tools for
manipulation are atomic force microscopes, magnetic tweezers,
photonic force microscopes (optical tweezers), micro needles,
electric fields and field cages, and levitated liquid droplets. To
control these manipulation tools, secondary light emitted by the
particle is often used as a feedback signal.
Apart from the manipulation of sample components, the
characterization of samples plays an important role in chemistry,
physics, biology, and medicine. Typical applications are chemical
analysis in medicine, forensic science, material science,
diagnostics, and biotechnology. Furthermore, in pre-clinical drug
development, biological target molecules are examined in screening
processes to identify compounds interacting with said target
molecules. Very often ligand-receptor, substrate-enzyme,
protein-protein, protein-DNA or protein-cell-membrane interactions
are studied. Such studies are often conducted utilizing secondary
light emission as a read-out. The information of emitted secondary
light can e.g. be used to produce images of the sample under study.
Presently, primarily fluorescence intensity is used in imaging.
Further secondary light emission parameters such as fluorescence
lifetime, anisotropy or polarization, or ratios of intensities from
different wavelengths are also often used.
In the following, the word "light" will sometimes be used instead
of "radiation". The word "light" shall not be constrained as being
limited to visible radiation unless otherwise specified.
The excitation of a sample under study can e.g. take place by
radiation as single-photon excitation, two-photon excitation or
multi-photon excitation, or by chemical reactions. The light used
for inducing a secondary light emission may be continuous or
sinusoidally modulated, e.g. for phase modulation measurements, or
it may be a series of light pulses. The scattering or emission of
secondary light after excitation by primary light can be an elastic
process, like Rayleigh-, Mie-, and Raman-scattering (e.g.
Surface-Enhanced-Raman-Scattering (SERS) or
Surface-Enhanced-Resonance-Raman-Scattering (SERRS)), or an
inelastic process, e.g. luminescence such as phosphorescence or
fluorescence. These processes are typically induced by directing
electromagnetic radiation (e.g. appropriate laser light) as primary
light onto the sample. Whereas elastic emission is a temporally
prompt process, inelastic emission is generally delayed with
respect to the excitation time. In case of luminescence, the
probability of electronic deactivation and hence the inelastic
emission of light is temporally exponentially distributed. The
lifetime of the electronically excited state is defined as the time
where the probability to be in the excited state has dropped to
1/e.
The detection of secondary emitted light can e.g. be performed on
an epi-illuminated confocal fluorescence microscope using avalanche
photodiodes as described in detail previously [Kask, P., Palo, K.,
Fay, N., Brand, L., Mets, U., Ullman, D., Jungmann, J., Pschorr, J.
and Gall, K. (2000) Two-Dimensional Fluorescence Intensity
Distribution Analysis: Theory and Applications. Biophys. J., 78,
1703-1713]. Thereby, the excitation light can be in a stationary
position, or be moved over and scanning the sample. Further
possible set-ups are evanescent-excitation, Raman microscopes,
near-field microscopes, scanning (e.g. near-field or confocal)
microscopes using beam-scanners, table-scanners, and/or
Nipkov-devices, as well as spectrometers using non-confocal
excitation and detection. Detection might also be performed on the
opposite side of the excitation. Also, the detector does not
necessarily have to be an avalanche photodiode. Any sensitive
detector such as photo-multipliers or CCD-cameras will do.
To be detected due to emitted secondary light, the particle of
interest either has to have the ability to emit light by itself or
has to be labeled by a secondary light emitting tag, e.g. a
fluorescent dye, a luminescent nanoparticle (e.g. a seminconductor
quantum dot), or a metal chelate. In general, the particles of
interest are observed in a medium such as in a solution, on
surfaces, on cells, or in matrices. In drug screening processes,
typically the interaction between a biological target and a
luminescent ligand in the presence of low molecular weight
compounds is studied. The biological target is typically involved
in the pathogenesis of a disease and the compounds are screened to
find possible drug candidates. In one typical experimental set-up,
the influence of the compounds on the binding reaction between
ligand and target is studied utilizing secondary light emission as
a read-out.
There are two main disadvantages when using secondary emitted light
such as fluorescence to perform characterizations of biological
and/or chemical samples. (1) "Auto-fluorescence": Background light
might occur due to additional secondary light emitting particles in
the sample besides the particles of interest. These particles might
be the medium itself, i.e. solvent or surface molecules,
impurities, and/or the added compounds. In the field of
fluorescence detection, this phenomenon is known as
auto-fluorescence. The auto-fluorescence interferes the detected
signal which does not solely consist of actual light from the
particles of interest anymore. Since the interfering
auto-fluorescence has its own characteristics, the read-out (such
as intensity, anisotropy, brightness, or lifetime) will be
deteriorated. Let us consider the following example for
illustration purposes only: The particles of interest might emit
light with intensity I.sub.1=120 kHz, anisotropy r.sub.1=0.15,
brightness q.sub.1=30 kHz, and lifetime .tau..sub.1=3 ns. The
unwanted auto-fluorescence might have an intensity I.sub.2=80 kHz,
anisotropy r.sub.2=0.05, brightness q.sub.2=2 kHz, and lifetime
.tau..sub.2=1 ns. Then the non-deteriorated read-out without
interfering auto-fluorescence would be I.sub.tot=120 kHz,
anisotropy r.sub.tot=0.15, brightness q.sub.tot=30 kHz, and
lifetime r.sub.tot=3 ns. The deteriorated read-out with interfering
auto-fluorescence could be approximated via the fraction of
background light, f.sub.2=I.sub.2/I.sub.tot=0.4 (with
I.sub.tot=I.sub.1+I.sub.2), with
x.sub.tot=x.sub.1.times.(1-f.sub.2)+x.sub.2.times.f.sub.2 (with
x=r, q, .tau.); thus, I.sub.tot=200 kHz, anisotropy r.sub.tot=0.11,
brightness q.sub.tot=18.8 kHz, and lifetime .tau..sub.tot=2.2
ns.
Samples with auto-fluorescence will therefore exhibit an increased
light intensity. However, the scientist might not know that the
secondary light contains spurious elements due to
auto-fluorescence. The correct characterization of the particles of
interest via the characteristics of the emitted light will be
deteriorated and will fail. (2) "Quenching": In the case of tagged
particles, the added compounds might directly react with the
secondary light emitting tag and not with the tagged particle
itself. This reaction might lead to a change in the secondary light
emission, mainly a decrease in light intensity. Possible reactions
can be ground-state and excited-state complexes. In the field of
fluorescence, this phenomenon is known as quenching. Thus, changes
and characteristics in the secondary emitted light do not come from
variations or properties of the tagged particle of interest
anymore, but from the quenching reaction between compound and
secondary light emitting tag. Again, let us consider an example for
illustration purposes only: Fluorescently labeled peptides might
emit light with intensity I.sub.1=100 kHz, anisotropy r.sub.1=0.08,
brightness q.sub.1=30 kHz, and lifetime .tau..sub.1=3 ns. Upon
binding to a protein, the characteristics of the emitted light
might change to I.sub.2=50 kHz, anisotropy r.sub.2=0.20, brightness
q.sub.2=15 kHz, and lifetime .tau..sub.2=1.5 ns The binding might
be activated by certain compounds. Thus, an activating compound
could directly be observed by the characteristics of the emitted
light due to the changes caused by the binding event. However,
imagine a non-activating compound which directly quenches the
fluorescent tag. This compound might also induce changes in the
emitted light, e.g. a decreased intensity and brightness, although
no binding event occurred. From the characteristics of the read-out
an alleged activation would be observed.
Samples with quenching compounds will exhibit a change in the
emitted light, mainly a decreased light intensity. Again, the
correct characterization of the tagged particles of interest via
the characteristics of the emitted light will be deteriorated and
will fail.
In addition to the above described cases of auto-fluorescence and
quenching, a general deterioration of the signal might occur e.g.
due to sample handling mistakes such as pipetting errors or due to
bleaching effects of fluorescent dyes. A dye is bleached if the
exciting light is causing an irreversible or reversible reaction.
This reaction leads to a change in the light emission e.g. by a
destruction of the dye. In the case of a destruction, the dye would
irreversibly loose its ability to emit light.
In particular, in the field of high throughput drug screening, a
deteriorated signal will have a severe impact on the further
pre-clinical and clinical development. False positive compounds
might be further optimized with high technical and financial
efforts. False negative compounds might never become drugs because
they have not been identified in the primary screening process. Of
course, also in diagnostic and forensic applications interfering
secondary light emission might have severe impacts on the data and
therefore on the outcome of an experiment.
It is therefore an object of the present invention to improve the
reliability of experimental data, in particular to improve the
light emission read-out with respect to impacts of interfering
effects on secondary light emission, in particular deterioration
such as auto-fluorescence or quenching. This object is solved by
the invention according to the independent claims. Advantageous
embodiments of the invention are characterized in the dependent
claims.
According to the present invention, a method is provided for
identifying the impacts of interfering effects on experimental
data. The method comprises the steps of: (i) providing experimental
data, (ii) determining values of one or a plurality of
identification parameters from said data, (iii) creating a
histogram or distribution of the values of the identification
parameters, (iv) determining one or a plurality of thresholds for
the values of identification parameters from said histogram or
distribution, which thresholds are indicative for the interfering
effects, (v) analyzing the values of one or a plurality of
identification parameters whether or not these values fulfill one
or a plurality of criteria with respect to the thresholds, and (vi)
determining those data which are influenced and/or those data which
are not affected by the interfering effects.
In another aspect according to the present invention, a method is
provided for detecting the impacts of auto-fluorescence and/or
fluorescence quenching on experimental data resulting from
fluorescence experiments. The method comprises the steps of: (i)
providing the experimental data comprising a plurality of data
sets, (ii) determining values of one or a plurality of
identification parameters from said data sets, (iii) creating a
histogram or distribution of the values of the identification
parameters, (iv) determining one or a plurality of first thresholds
for the values of identification parameters from said histogram or
distribution, which first thresholds are indicative for
auto-fluorescence, and/or determining one or a plurality of second
thresholds for the values of identification parameters from said
histogram or distribution, which second thresholds are indicative
for fluorescence quenching, (v) analyzing the values of one or a
plurality of identification parameters whether or not these fulfill
one or a plurality of criteria with respect to the thresholds, and
(vi) determining those data sets which are influenced and/or those
data sets which are not affected by auto-fluorescence and/or
fluorescence quenching.
In still another aspect of the present invention, a method is
provided for detecting false positive and/or false negative results
in experimental data. These data might result from screening of
potentially pharmaceutical active compounds. The data might also
e.g. result from diagnostic tests or forensic studies. The method
comprises the steps of: (i) providing the data, (ii) determining
values of one or a plurality of identification parameters from said
data, (iii) creating a histogram or distribution of the values of
the identification parameters, (iv) determining one or a plurality
of first thresholds for the values of identification parameters
from said histogram or distribution, which first thresholds are
indicative for a false-positive result, and/or determining one or a
plurality of second thresholds for the values of identification
parameters from said histogram or distribution, which second
thresholds are indicative for a false-negative result, (v)
analyzing the values of one or a plurality of identification
parameters whether or not these fulfill one or a plurality of
criteria with respect to the thresholds, and (vi) determining those
data which represent a false-positive result and/or those data
which represent a false-negative result.
In still another aspect, the invention provides a system for
detecting the impacts of interfering effects on experimental data
resulting from optical experiments. The system comprises: (i) means
for supporting one or a plurality of samples in an inspection
station, (ii) one or a plurality of photosensitive detectors which
are positioned relative to the inspection station so that
electromagnetic radiation emitted from the samples impinges on the
detectors, (iii) means for addressing the photosensitive detectors
to generate experimental data, (iv) means for determining values of
one or a plurality of identification parameters from said data, (v)
means for storing the values in such a manner that preferably all
the values which relate to any one of the samples are linked, (vi)
means for creating a histogram or distribution of the values of the
identification parameters, (vii) means for determining one or a
plurality of thresholds for the values of identification parameters
from said histogram or distribution, which thresholds are
indicative for the interfering effects, (viii) means for analyzing
the values of one or a plurality of identification parameters
whether or not these fulfill one or a plurality of criteria with
respect to the thresholds, and (ix) means for supplying as output
information those data which are influenced and/or those data which
are not affected by the interfering effects.
In a preferred embodiment, the identification parameter is selected
from the group consisting of a fluorescence intensity, a ratio of
fluorescence intensities at selected wavelengths, a ratio of
fluorescence intensities at different polarization directions, a
fluorescence anisotropy, a fluorescence polarization, a
fluorescence lifetime, a rotational correlation time, a diffusion
constant, a concentration of fluorophores, and a specific
fluorescence brightness. In another preferred embodiment, a
function of the aforementioned members of the group might be chosen
as an identification parameter.
The most simple identification parameters are: (a) The signal count
rate, denoted intensity, I. Consequently, the experimental data can
be checked whether they fulfill certain criteria as follows. The
values of the identification parameter, in the present case the
intensity values, can be checked with respect to one or a plurality
of thresholds for the values of the intensity as an identification
parameter, e.g. a pre-selected intensity value and/or intensity
function. (b) The anisotropy, r, or polarization, P. When employing
two detectors which monitor different polarization directions of
the emitted light, r and P can be calculated from the intensities
with parallel, I.sub.P, and perpendicular, I.sub.S, polarization
with respect to the polarization of the exciting light.
Consequently, the data resulting from these optical experiments can
be checked with the help of the identification parameter whether
they fulfill certain criteria with respect to one or a plurality of
thresholds. In the present case, these thresholds can be
pre-selected anisotropy or polarization values. Anisotropy and
polarization are typically defined as follows:
r=(I.sub.P-I.sub.S)/(I.sub.P+2I.sub.S)P=(I.sub.P/+I.sub.S) (c) The
ratio of intensities, f. When employing at least two detectors
which monitor different wavelength ranges or different polarization
directions of the emitted light, ratios or fractions of intensities
detected on one or more detectors can be deduced. These can be
checked whether they are in consistence with a threshold, such as a
pre-selected value and/or function of intensity ratios. (d) The
lifetime, .tau.. The mean excitation-to-detection delay time, i.e.
the lifetime of the excited state of the secondary light emitting
particle can e.g. be measured using pulsed light excitation
together with time-correlated-single-photon-counting (TCSPC) or
using modulated light excitation in general. The lifetime can be
determined by a fit to the excitation-to-detection delay time
histogram and even enables to distinguish secondary light emitting
particles with different lifetimes within a mixture and to quantify
them via their fractional intensities; this can preferably be done
by performing a multi-component fit to the excitation-to-detection
delay time histogram. Again, a check of the values of the
identification parameters of the experimental data with regard to a
pre-selected lifetime value and/or function can be conducted. (e)
The rotational correlation time, r. The rotational correlation time
is directly linked to the rotational diffusion of the light
emitting particles and is therefore a very nice tool to distinguish
molecules of different rotational diffusion e.g. due to different
mass. It can for example be determined using time-resolved
anisotropy analysis. Time-resolved anisotropy is based on the same
measurement principle as in the lifetime analysis. One can
determine the rotational correlation time by globally analyzing the
two excitation-to-detection delay time histograms recorded in the
two different detection channels monitoring different polarization
directions of the emitted light. This analysis enables to
distinguish secondary light emitting particles with different
lifetimes and/or rotational correlation times within a mixture and
to quantify them via their fractional intensities; this can
preferably be done by performing a multi-component fit to the
excitation-to-detection delay time histograms. Again, a check of
the values of the identification parameters of the experimental
data with regard to a pre-selected lifetime and/or rotational
correlation time value and/or function can be conducted.
More complex spectroscopic techniques have been developed which are
based on the detection of single light emitting particles and which
enable to resolve different light emitting particles within the
same sample. (a) The direct observation of signal bursts from
single light emitting particles enables to qualitatively and
quantitatively identify different light emitting particles in a
mixture via their spectroscopic properties; e.g. such as realized
in a dye mixture using the differing fluorescence properties:
lifetime [Zander, C., Sauer, M., Drexhage, K. H., Ko, D. S.,
Schulz, A., Wolfrum, J., Brand, L., Eggeling, C. and Seidel, C. A.
M. (1996) Detection and characterization of single molecules in
aqueous solution. Appl. Phys. B, 63, 517-523], lifetime and
intensity [Fries, J. R., Brand, L., Eggeling, C., Kollner, M. and
Seidel, C. A. M. (1998) Quantitative identification of different
single-molecules by selective time-resolved confocal fluorescence
spectroscopy. J. Phys. Chem. A, 102, 6601-6613], and anisotropy
[Schaffer, J., Volkmer, A., Eggeling, C., Subramaniam, V., Striker,
G. and Seidel, C. A. M. (1999) Identification of single molecules
in aqueous solution by time-resolved fluorescence anisotropy. J.
Phys. Chem. A, 103, 331-336]. Accordingly, a suitable
identification parameter as well as its value being indicative for
a certain effect is chosen. The experimental data can be checked
with the help of one or a plurality of corresponding identification
parameters (e.g. lifetime; lifetime and intensity; anisotropy) for
fulfilling certain criteria with respect to one or a plurality of
thresholds, such as a pre-selected value of lifetime. (b) FCS
(fluorescence correlation spectroscopy) analyses the temporal
characteristics of signal fluctuations from single light emitting
particles. The calculated correlation function of these
fluctuations decays with time constants that are characteristic of
the molecular processes causing these signal changes, e.g.
diffusion into and out of the detection volume and reaction
kinetics. The amplitude of the decay is related to the molecular
concentration while the inflection point of the correlation
function represents the mean diffusion time, .tau..sub.diff, of the
fluorescing molecules through the detection volume, which is
dependent on the diffusion coefficient. Hence, by a fit to the
correlation function, FCS is able to resolve components of a sample
with different diffusion coefficients due to their different
molecular masses; in practice, preferably a multi-component fit to
the correlation function is performed. Accordingly, when studying
FCS data, a suitable identification parameter is the diffusion
coefficient. (c) FIDA or 1D-FIDA (fluorescence intensity
distribution analysis) relies on a collection of instantaneous
values of the fluctuating intensity by building up a frequency
histogram of the signal amplitudes throughout a measurement [Kask,
P., Palo, K., Ullman, D. and Gall, K. (1999) Fluorescence-intensity
distribution analysis and its application in biomolecular detection
technology. Proc. Natl. Acad. Sci. U.S.A., 96, 13756-13761]. The
resulting distribution of signal intensities is then analyzed by a
theory which relates specific fluorescence brightness q (intensity
per molecule in kHz), and absolute concentration c (mean number of
molecules in the detection volume) of the molecules under
investigation. By performing a fit to the frequency histogram, FIDA
distinguishes species of the sample according to their different
values of specific molecular brightness q; in practice, preferably
a multi-component fit is performed. Consequently, when studying
data collected by FIDA experiments, a suitable identification
parameter is the specific molecular brightness q. (d) Further
methods such as 2D-FIDA (two-dimensional fluorescence intensity
distribution analysis), FIMDA (fluorescence intensity multiple
distribution analysis), or FILDA (fluorescence intensity and
lifetime distribution analysis) might be applied resulting in
improved performance compared to the FIDA technique. 2D-FIDA
typically makes use of a two-detector set-up monitoring either
different polarization or emission bands of the signal. In addition
to the FIDA performance, 2D-FIDA achieves additional molecular
resolution by performing a multi-component fit to the
two-dimensional frequency histogram of the concurrent signal
amplitudes from both detectors and, thus, the concurrent
determination of two specific brightness values of each detection
channel, q.sub.1(channel1) and q.sub.2(channel2), for each
component [Kask, P., Palo, K., Fay, N., Brand, L., Mets, U.,
Ullman, D., Jungmann, J., Pschorr, J. and Gall, K. (2000)
Two-Dimensional Fluorescence Intensity Distribution Analysis:
Theory and Applications. Biophys. J., 78, 1703-1713]. By observing
the molecular resolved anisotropy, simple and more complex binding
events and enzymatic reactions may be followed. Alternatively, such
events may be followed using light emitting particles with
different emission bands and combining this with either two-color
excitation by different lasers or energy-transfer interaction.
Thus, the use of a second detector can improve the power of FIDA to
distinguish between molecular components and is, therefore,
increasingly applied in high-performance drug discovery. When
analyzing data collected by 2D-FIDA experiments, one will
preferably choose two identification parameters: (i) a molecular
brightness, q.sub.1, at a first wavelength and/or a molecular
brightness, q.sub.2, at a second wavelength; alternatively (ii) a
molecular brightness, q.sub.1, at a first polarization and/or a
molecular brightness, q.sub.2, at a second polarization. FIMDA
typically only demands one detection channel and extracts all
characteristics of both FCS and FIDA, i.e. diffusion time,
.tau..sub.diff, specific molecular brightness, q, and absolute
concentration, c, from a single measurement [Palo, K., Mets, U.,
Jager, S., Kask, P. and Gall, K. (2000) Fluorescence Intensity
Multiple Distribution Analysis: Concurrent Determination of
Diffusion Times and Molecular Brightness. Biophys. J. 79]. This is
achieved by fitting a series of different FIDA histograms obtained
from the same measurement regarding different components. FIMDA
increases the readout and improves likelihood of molecular
resolution of different components of the sample effectively by one
dimension. Therefore, when analyzing data collected by a FIMDA
experiment, one will typically choose as identification parameters
a specific molecular brightness and/or a diffusion time. FILDA as
well typically only demands one detection channel and extracts all
characteristics of both FIDA and lifetime determination, i.e.
specific molecular brightness, q, lifetime, .tau., and absolute
concentration, c, from a single measurement. FILDA is based on
fitting a two-dimensional histogram of the number of photons
detected in counting time intervals of given width and the sum of
excitation-to-detection delay times of these photons, once again
regarding and quantifying different fluorescent components. The
combined information yielded by FILDA results in significantly
increased accuracy compared to that of FIDA and lifetime analysis
alone. Consequently, when analyzing FILDA data, one may choose as
identification parameters a lifetime and/or a specific molecular
brightness.
In all of the above methods, which can quantify each component by
its concentration, c, or an according amplitude, the concentration
or amplitude can as well be taken as an identification
parameter.
With regard to optical experiments, the most simple identification
parameter for auto-fluorescence, fluorescence quenching, and/or
other general deterioration of the measured data (e.g. through
measuring errors, dispensing or pipetting errors, etc) is the light
intensity, since these sorts of deterioration lead to a change of
the experimentally determined intensity; in principle, an increase
is assumed in the case of auto-fluorescence and a decrease in the
case of fluorescence quenching.
However, since auto-fluorescence and quenching change the whole
characteristic of the emitted and, thus, detected light, other
read-out parameters can as well be used for identification. These
are for example anisotropy (r), polarization (P), ratio of
intensities (f), lifetime (.tau.), rotational correlation time
(.rho.), brightness (q), concentration (c), brightness values of
different detection channels (q.sub.1 and q.sub.2), diffusion time
(.tau..sub.diff), or other parameters resulting from a fit to the
lifetime histogram, correlation function (FCS), FIDA-, or other
histogram techniques (e.g. 2D-FIDA, FIMDA, or FILDA).
The identification parameter can preferably be a quality parameter
of such a fit such as a chi.sup.2-value, which is for example
calculated from
.times..times..function..function. .function..function.
##EQU00001## (where the sum is performed over all data points x,
{circumflex over (P)}(x) is the measured data, P(x) the theoretical
data, and W(x) are the weights, e.g. expressed as W(x)=M/P(x) with
the total number of data points, M).
Further possible identification parameters result from a
moment-analysis to the lifetime histogram, correlation function
(FCS), FIDA-, 2D-FIDA-, FIMDA-, or FILDA-histogram by calculating
moments, correlations, cumulants, and functions of these such as
M.sub.1.sup.2/(M.sub.2-M.sub.1.sup.2),
(M.sub.2-M.sub.1)/(M.sub.1T), and
K.sub.3.times.0.55.times.K.sub.1/K.sub.2.sup.2 with the first and
second moments, M.sub.1 and M.sub.2, and the first, second, and
third factorial cumulants, K.sub.1, K.sub.2, and K.sub.3, resulting
from a one-dimensional function such as the FIDA-histogram.
|K.sub.10+K.sub.01-(K.sub.20+2K.sub.11+K.sub.02).sup.2/[(K.sub.30+3K.sub.-
21+3K.sub.12+K.sub.03).times.0.55]| with the factorial cumulants,
K.sub.xy, resulting from a two-dimensional function such as the
2D-FIDA-histogram.
In the case of observing images of the sample, additional
identification parameters besides the ones mentioned above might
come from all kinds of pattern recognition or image analysis
algorithms as well as from image moment analysis.
The values of several identification parameters can also be linked
to obtain a new value of a single identification parameter.
Examples are mathematical procedures such as the calculation of
vector lengths or of generalized square distances.
Further identification parameters can also be obtained by relating
one or more of the above parameters to their values obtained from
control samples such as [X(sample)-X(control B)]/[X(control
A)-X(control B)] or [X(control A)-X(sample)]/[X(control
A)-X(control B)] where X(sample) is the identification parameter
obtained from the sample and X(control A) and X(control B) are the
identification parameters obtained from the two different control
samples. For example, in the case of the binding of a tagged ligand
to a protein, the latter relation expresses the inhibition of the
binding if the control A sample represents the complete binding
event and the control B the free ligand.
Data from an experiment can be classified to have been influenced
by auto-fluorescence, quenching, and/or general deterioration, if
e.g. the value of at least one of the above identification
parameters determined from the emitted and detected light of this
sample is above or below a pre-selected threshold value. In case a
set of various identification parameters is used for
classification, the classification rules might be that the values
of all of the identification parameters have to be above or below
certain corresponding threshold values, that only the value of at
least one identification parameter has to be above or below a
certain corresponding threshold value, or that the set of
identification parameters have to fulfill certain functions or
relations between the corresponding parameters. For example, if two
identification parameters, x.sub.1 and x.sub.2, are used, the
classification rules might be (a) [x.sub.1>t_up(x.sub.1) and/or
x.sub.1<t_low(x.sub.1)] and/or [x.sub.2>t_up(x.sub.2) and/or
x.sub.2<t_low(x.sub.2)] with upper and lower threshold values,
t_up and t_low, of x.sub.1 and x.sub.2, respectively. (b)
x.sub.2>f.sub.1(x.sub.1) and/or x.sub.2<f.sub.2(x.sub.1)
where f.sub.1 and f.sub.2 are functions of one of the parameters
such as f.sub.i(x.sub.1)=a.sub.i+m.sub.i.times.x.sub.1 with i=1 and
2 and constants a.sub.i and m.sub.i.
The pre-selected threshold values, functions and/or relations can
be determined from the values of identification parameters obtained
from a whole set of observed samples. The threshold values,
functions and/or relations can be determined from the
one-dimensional distribution of the values of one identification
parameter or from the multi-dimensional distribution of the values
of a set of concurrent identification parameters as obtained from
all or parts of the set of observed samples.
The values of the distribution of identification parameters as
determined from the set of observed samples can also be
mathematically transformed or normalized to yield special
properties of the distribution such as Gaussian distributions, e.g.
by calculating standardized or studentized residuals.
The set of observed samples (and consequently the gathered
experimental data or data sets) can either be all or parts of the
samples to be analyzed and/or all or parts of control samples.
Histograms or distributions of the values of the identification
parameters can therefore be created from all of the data sets, e.g.
including also control samples, or only parts thereof.
The threshold values, functions and/or relations can be derived
from the distribution through functions of the mean, median,
moments, cumulants, standard deviation, and/or the values
themselves of the distribution. A possible function would be
mean.+-.y.times.s or median.+-.y.times.s*, where y is a constant,
e.g. 3, and mean and median are the mean and median of the
distribution, respectively, s is the standard deviation of the
distribution, and s* represents the median like standard deviation
of the distribution. s* can either be obtained by (a) cutting of x
% of the edges of the distribution (i.e., disregarding the x %
highest and lowest values, x is a constant and can e.g. be 1) and
calculating the common standard deviation of the remaining
distribution, (b) calculating the median of the absolute
differences between each point and the median of the distribution
and multiplying this value by 1.482, (c) fitting a theoretical
distribution which is a function of s* to the experimental
distribution, such as the Gaussian distribution
(G(x)=A.times.exp(-2(x-x.sub.0).sup.2/(2s*.sup.2)) with an
amplitude, A, and the mean, x.sub.0 or (d) taking the mean of the
values calculated for s, in (a), (b), and/or (c). The constant, y,
can be set by hand after observation of the distribution or derived
from theory. For example, if the distribution of identification
parameters is Gaussian-like or has been mathematically transformed
or normalized to a Gaussian-like distribution, the threshold is
best set to y=3. In this case, the probability to be a valid part
of the distribution is 99.8%, if the value of an identification
parameter is within the threshold (95.5% for y=2 and 70.5% for
y=1).
For example, if only one identification parameter is chosen for
identification, the corresponding one-dimensional distribution can
be built up from the values of the identification parameter
obtained from several samples and be transformed to a Gaussian
distribution, and the upper and lower threshold values be
determined as mean+3.times.standard deviation and
mean-3.times.standard deviation, respectively.
For example, if two identification parameters are chosen, one can
a) build up the corresponding two-dimensional distribution from the
values of the identification parameters obtained from a set of two
different control samples (control A and control B), b) determine
the mean values (m(control A,x.sub.i), m(control A,x.sub.i)) from
each set of the two control samples for each identification
parameter i=1 and 2, c) determine upper (t_up(control A,x.sub.i),
t_up(control B,x.sub.i)) and lower threshold values (t_low(control
A,x.sub.i), t_low(control B,x.sub.i)) from each set of the two
control samples for each identification parameter i=1 and 2 (e.g.
by calculating mean+3.times.standard deviation and
mean-3.times.standard deviation, respectively, for each set), and
d) classify auto-fluorescence, quenching, and/or general
deterioration according to the condition;
x.sub.2<a.sub.1+m.sub.1.times.x.sub.1 or
x.sub.2>a.sub.2+m.sub.2.times.x.sub.1 or
[x.sub.1<t_low(control A,x.sub.1) and x.sub.1<t_low(control
B,x.sub.1)] or [x.sub.1>t_up(control A,x.sub.1) and
x.sub.1>t_up(control B,x.sub.1)] with m.sub.1=[t_low(control
A,x.sub.2)-t_low(control B,x.sub.2)]/[m(control
A,x.sub.2)-m(control A,x.sub.2)], a.sub.1=t_low(control
A,x.sub.2)-m.sub.1.times.m(control A,x.sub.2) m.sub.2=[t_up(control
A,x.sub.2)-t_up(control B,x.sub.2)]/[m(control A,x.sub.2)-m(control
A,x.sub.2)], a.sub.2=t_up(control
A,x.sub.2)-m.sub.2.times.m(control A,x.sub.2).
Preferably, one of the identification parameters should be the
intensity (I) (or the intensity normalized to the intensity
obtained from control samples as described above), whereby
auto-fluorescence is identified by an increased intensity, whereas
fluorescence quenching and/or general deterioration is identified
by a decreased intensity.
The identification step can of course not only be applied to the
said identification of general deterioration but in general to
check the failure of any signal or analysis method such as a fit.
For example, the results of any lifetime, FCS, FIDA, or further
histogram-based analysis can be checked in this manner for a
failure.
In summary, the identification step relating to data gathered from
fluorescence measurements is preferably performed in three steps.
1. Selection of at least one appropriate identification parameter,
one of which is preferably intensity (I) or normalized intensity.
2. Determination of pre-selected threshold values, functions,
and/or relations from the values of said chosen parameters. 3.
Identification of auto-fluorescence and/or fluorescence quenching
according to conditions specified by the said threshold values,
functions, and/or relations.
It is particularly preferred to conduct after the identification of
data being influenced by auto-fluorescence, fluorescence quenching,
and/or general deterioration, the following steps: 1. Correction of
the read-out--in the case of auto-fluorescence with the goal to
separate auto-fluorescence from the light emitted by the particles
of interest. 2. Test-procedure to check whether the correction
procedure has succeeded.
The correction step is performed for correcting the signal to
typically separate interfering signal and extract only the
information coherent with the light emitted from the particles of
interest.
The correction step for interfering auto-fluorescence signal
preferably demands a read-out which is able to distinguish
secondary light emitting particles with different emission
characteristics within the same sample and to quantify them using a
multi-component analysis, i.e. which molecularly resolves the
detected light. As mentioned above, read-out methods that are
capable of this molecular resolution are e.g. the lifetime
determination, FCS, FIDA, and further histogram-based methods such
as time-resolved anisotropy, 2D-FIDA, FIMDA, or FILDA. These
read-out methods enable to apply a multi-component fit to the
functions or histograms obtained from the detected light and, thus,
to resolve distinguishable light emitting particles.
Furthermore, the lifetime analysis based methods (lifetime
determination and FILDA) enable to distinguish between an elastic
light emission process such as scattering and an inelastic emission
process such as luminescence, since the elastic emission is a
temporally prompt process (lifetime .tau.=0 ns) while the inelastic
emission is generally delayed with respect to the excitation time
(.tau.>0 ns). Therefore, lifetime analysis offers the
possibility to explicitly regard elastic light emitting
particles.
In contrast, all brightness-based methods allow to explicitly
regard light emitting particles with high concentration and very
low brightness (detected counts per particle) such as scattering
solvent particles, since the signal amplitudes originating from
such components are simply Poissonian distributed. Such a signal
shall be denoted FIDA-background later on. In general, this
FIDA-background is fixed to a pre-selected value, e.g. determined
from control experiments.
The correction procedure is applied by preferably adding an
additional component to the fit, which accounts for the additional
auto-fluorescence "component". Thus, the remaining components/light
are cleared from the interfering auto-fluorescence light. According
to the read-out method, this additional component might be
accounted for in the fit as follows: lifetime determination: an
additional lifetime, which is either fixed to a pre-selected value
or freely fitted, and/or a freely fitted amount of elastic light
emitting particles. FCS: an additional diffusion time, which is
either fixed to a pre-selected value or freely fitted; FIDA: an
additional brightness, which is either fixed to a pre-selected
value or freely fitted, and/or a freely fitted FIDA-background.
2D-FIDA: an additional pair of brightness values, where either both
values are fixed to pre-selected values, only one value is fixed
while the other is subject to fitting, or both values are subject
to fitting, and/or a pair of FIDA-background values, where either
both values are fixed to pre-selected values, only one value is
fixed while the other is subject to fitting, or both values are
subject to fitting. histogram-based methods in general: an
additional set of read-out parameters (e.g. brightness, lifetime,
diffusion time, and/or FIDA-background), where either all values
are fixed to pre-selected values, only at least one value is fixed
while the others are subject to fitting, or all values are subject
to fitting.
In general, the light emission of the auto-fluorescent particles is
rather weak compared to the actual light emitting particles of
interest. Therefore, their brightness can be assumed to be rather
low, and, if fixed within a FIDA(-based) fit, the brightness values
of the additional auto-fluorescent component can be fixed to a
rather low value (e.g. >0 kHz to <10 kHz).
Furthermore, auto-fluorescent particles are very often highly
concentrated within the sample (e.g., in screening and HTS
applications compounds are added in .mu.M-concentration, while the
light emitting particles of interest are concentrated at least
below 50 nM for FIDA-based applications). Therefore, the
auto-fluorescence emission is very FIDA-background like and can be
considered as FIDA-background in the fit or as an additional
component with its concentration value fixed to a rather high value
(c>50).
Since an additional component might deteriorate the accuracy of the
results of the fit, it is very often preferable to apply this
auto-fluorescence correction procedure only to those data which are
identified to reveal auto-fluorescence properties. This means as a
general rule that preferably a correction step is only performed on
those data which have been identified as being influenced by
interfering effects, such as auto-fluorescence or fluorescence
quenching.
As outlined above, preferably a test procedure to check whether the
correction step has succeeded is performed in the same manner as
the identification step described above. Basically, failed
correction procedures are identified and marked as bad data
points.
In a first step, at least one parameter is chosen which identifies
the failure of the correction procedure, denoted failure parameter.
The potential failure parameters can be the same as previously
described as identification parameters in the identification step.
Preferably, at least one of the chosen failure parameters is a
resulting value of the applied fit, e.g., a quality parameter such
as the chi.sup.2 value, the lifetime, rotational correlation time,
diffusion time, a brightness value, or the concentration.
In a second step, threshold values, functions, and/or relations are
specified from the said chosen failure parameters as described in
detail in the identification step, i.e. by determining them from
the distribution of values of failure parameters obtained from a
whole set of observed samples or from subgroups thereof.
In a third step, a failure is classified according to conditions
specified by the said threshold values, functions, and/or
relations--a procedure that is analogue to the identification
procedure. This procedure has in detail been described above.
The test procedure can of course not only be applied to the said
correction procedure but in general to check the failure of any
analysis method such as a fit. For example, the results of any
lifetime, FCS, FIDA, or histogram-based analysis can be checked in
this manner for a failure.
Other objects, advantages, and novel features of the invention will
become apparent from the following detailed description of the
invention when taken in conjunction with the accompanying
drawings.
FIGS. 1A-F illustrates impacts of the effect of quenching compounds
on fluorescence emission data and presents different identification
methods.
FIGS. 2A-E illustrates impacts of the effect of auto-fluorescent
compounds on fluorescence emission data using polarization and
2D-FIDA read-outs and presents different identification methods, a
correction procedure, and a procedure for checking possible
failures of the correction step.
FIGS. 3A-B illustrates impacts of the effect of auto-fluorescent
compounds on fluorescence emission data using FIDA read-outs and
presents a correction procedure.
FIGS. 4A-B illustrates screening for activating compounds using
polarization and 2D-FIDA read-outs and the identification of
auto-fluorescent and quenching compounds, a correction procedure,
and a check of the correction step in the case of
auto-fluorescence.
FIGS. 5A-B illustrates an identification of auto-fluorescent and
quenching compounds in high-throughput-screening (HTS).
FIG. 6 shows a schematic diagram of a preferred system for
detecting the impacts of interfering effects on experimental data
resulting from fluorescence measurements.
EXAMPLES
The measurements presented in the following were performed on an
epi-illuminated confocal fluorescence microscope as described in
[P. Kask, K. Palo, N. Fay, L. Brand, U. Mets, D. Ullmann, J.
Jungmann, J. Pschorr, K. Gall (2000) Biophys. J, 78, 1703-1713]. A
polarized continuous-wave (cw) laser either at 543 nm or 633 nm was
used to excite a fluorophore (Tetramethyl-Rhodamine (TAMRA) for 543
nm excitation, MR-121 for 633 nm excitation) alone or covalently
linked to a molecule of interest. Detection was performed with a
single detector or two detectors (Avalanche-Photo-Diode, APD)
monitoring the fluorescence light emitted with parallel or
perpendicular polarization with respect to the polarization of the
exciting light. While the one-detector set-up was used for the
fluorescence data analysis via FIDA, the two-detector set-up was
used for the determination of the polarization or anisotropy values
and analysis via 2D-FIDA.
Example 1
In a first measurement series, different amounts of various water
soluble chemical compounds were added to an aqueous TAMRA solution
(about 15 nM, resulting in 96 different samples) and the total
intensity, I.sub.tot, as well as the polarization, P, were
determined for the 96 different samples (two-detector set-up and
measurement time of two seconds). I.sub.tot and P were calculated
from the intensities with parallel, I.sub.P, and perpendicular,
I.sub.S, polarization with respect to the exciting light.
I.sub.tot=I.sub.P+2I.sub.SP=(I.sub.P-I.sub.S)/(I.sub.P+I.sub.S).times.100-
0 Subsequently, 4 different methods were applied to identify
samples with deteriorated signal as presented in FIG. 1.
FIG. 1A shows the one-dimensional distribution of I.sub.tot over
all 96 samples (dotted line) together with the thresholds (vertical
lines) for identification of deteriorated signal. The thresholds
were set according to the median, med(I.sub.tot)=1119.9 kHz, and
the median like standard deviation, s*(I.sub.tot)=145.8 kHz, of
I.sub.tot from all 96 samples (s* has been described previously);
threshold(I.sub.tot)=med(I.sub.tot).+-.3.times.s*(I.sub.tot). All
samples that exhibited a value of I.sub.tot outside these
thresholds were identified to be deteriorated, in this case 12
samples.
FIG. 1B shows the one-dimensional distribution of the
mathematically transformed total intensity, TI, together with a
Gaussian fit to the distribution (gray line;
G(TI)=A.times.exp[-(TI-TI.sub.0).sup.2/(2.sigma..sup.2)] with the
variables A, TI.sub.0, and .sigma. subject to fitting and resulting
in A=13.9, TI.sub.0=0.44, and .sigma.=0.3) and the thresholds
(vertical lines) for identification of deteriorated signal. The
mathematical transformation was performed according to the steps;
(a) calculation of the median, med(I.sub.tot), of I.sub.tot from
all 96 samples, (b) calculating the difference,
res(I.sub.tot)=I.sub.tot-med(I.sub.tot), for each sample, (c)
determination of the mean, mean(res(I.sub.tot)), and the standard
deviation, s(res(I.sub.tot)), of res(I.sub.tot) from all 96
samples, (d) calculating
TI=[res(I.sub.tot)-mean(res(I.sub.tot))]/s(res(I.sub.tot)). The
thresholds were determined from the resulting value of .sigma. and
TI.sub.0 of the Gaussian fit;
threshold(TI)=TI.sub.0.+-.3.times..sigma.. All samples that
exhibited a value of TI outside these thresholds were identified to
be deteriorated, in this case 13 samples.
FIG. 1C shows the one-dimensional distribution of the slightly
different mathematically transformed total intensity, TI*, together
with a Gaussian fit to the distribution (gray line;
G(TI*)=A.times.exp[-(TI*-TI.sub.0*).sup.2/(2.sigma.*.sup.2)] with
the variables A, TI.sub.0*, and .sigma.* subject to fitting and
resulting in A=3.788, TI.sub.0*=0.266, and .sigma.=1.11) and the
thresholds (vertical lines) for identification of deteriorated
signal. In this case, the mathematical transformation was performed
according to the steps; (a) calculation of the median,
med(I.sub.tot), of I.sub.tot from all 96 samples, (b) calculating
the difference, res(I.sub.tot)=I.sub.tot-med(I.sub.tot), for each
sample, (c) determination of the median, median(res(I.sub.tot)),
and the median like standard deviation, s*(res(I.sub.tot)), of
res(I.sub.tot) from all 96 samples, (d) calculating
TI*=[res(I.sub.tot)-median(res(I.sub.tot))]/s*(res(I.sub.tot)). The
thresholds were determined from the resulting value of .sigma.* and
TI.sub.0* of the Gaussian fit;
threshold(TI*)=TI.sub.0*.+-.3.times..sigma.*. All samples that
exhibited a value of TI outside these thresholds were identified to
be deteriorated, in this case 12 samples.
FIG. 1D represents the two-dimensional distribution of joint total
intensity-polarization pairs, (I.sub.tot, P), from all 96 samples
(black dots) together with the thresholds (black lines) for
identification of deteriorated signal. The thresholds were set
according to the median, med(I.sub.tot)=1119.9 kHz and
med(P)=30.72, and the median like standard deviation,
s*(I.sub.tot)=145.8 kHz and s*(P)=5.15, of I.sub.tot and P
respectively from all 96 samples;
threshold(I.sub.tot)=med(I.sub.tot).+-.3.times.s*(I.sub.tot) and
threshold(P)=med(P).+-.3.times.s*(P). All samples that exhibited a
value of I.sub.tot or P outside these thresholds were identified to
be deteriorated, in this case 13 samples.
By one or the other method, the same conspicuous samples were
identified by a decreased intensity and an increased polarization.
To find the reason behind this deterioration, one conspicuous
compound was added at rising concentrations to the dye solution.
The measured total intensity, I.sub.tot, and polarization, P, are
shown in FIGS. 1E and F, respectively. One clearly observes, that
the deterioration was caused by a quenching interference of the
compound to the fluorescence emission of the dye, which was a
decreasing intensity accompanied by an increase in
polarization.
Example 2
In a second measurement series, the binding of a small
MR-121-labeled peptide to the SH2-domain of the Grb2-protein was
monitored by a change in the fluorescence polarization, P, of the
MR-121 fluorescence emission (two-detector set-up, measurement time
of ten seconds). In the different samples, the binding was
increasingly inhibited by the titration of unlabeled peptide.
Thereby, nine different concentrations of unlabeled peptide were
measured five times each, i.e. 45 samples were observed. While one
set of 45 samples only contained the assay components (labeled and
unlabeled peptide and protein), auto-fluorescent compounds (1 .mu.M
Rhodamine 800) had been added to another set of 45 samples. 2D-FIDA
with a one-component fit was applied to the signal of all samples.
This analysis yielded values of concentration, c, brightness,
q.sub.1 and q.sub.2, of each detection channel monitoring the light
emission with parallel and perpendicular polarization with respect
to the exciting light, respectively, and of chi.sup.2, which is the
quality parameter of the fit (as presented previously). The total
signal intensity was once again calculated from the intensities
with parallel, I.sub.P, and perpendicular, I.sub.S, polarization
with respect to the exciting light, while the polarization, P, was
calculated from q.sub.1 and q.sub.2.
I.sub.tot=I.sub.P+2I.sub.SP=(q.sub.1-q.sub.2)/(q.sub.1+q.sub.2).times.100-
0 In addition, two control samples were measured ten times each,
resulting as well in values of c, q.sub.1 and q.sub.2, chi.sup.2,
I.sub.tot, and P. The ten high control samples, which contained
only labeled peptide and protein (resulting in mainly bound labeled
peptide), resulted in values of c(high), q.sub.1(high) and
q.sub.2(high), chi.sup.2(high), I.sub.tot(high), and P(high). The
low control, which contained labeled peptide, excess of unlabeled
peptide, and protein (resulting in totally inhibited binding, thus
mainly unbound labeled peptide), resulted in values of c(low),
q.sub.1(low) and q.sub.2(low), chi.sup.2(low), I.sub.tot(low), and
P(low). This enabled the calculation of the normalized total
intensity, NI, and the inhibition, Inh, for each measurement X.
NI(X)=[I.sub.tot(X)-I.sub.tot(low)]/[I.sub.tot(high)-I.sub.tot(low)].time-
s.100 Inh(X)=[P(high)-P(X)]/[P(high)-P(low)].times.100
FIGS. 2A and B present two different methods how samples with
auto-fluorescence can be identified.
FIG. 2A plots the two-dimensional distribution of joint normalized
total intensity-inhibition pairs, (NI, Inh), from both sets of 45
samples (black dots), the high samples (gray cross), and the low
samples (gray circles) together with the threshold functions (black
lines) for said identification. The thresholds were set by the
mean, m(NI,high)=0, m(Inh,high)=0, m(NI,low)=100, and
m(Inh,low)=100, and the standard deviation, s(NI,high)=19.7,
s(Inh,high)=5.8, s(NI,low)=17.9, and s(Inh,low)=2.7, of NI and Inh
from all ten high and low samples, respectively;
t_1(Inh)=m(Inh,high)-3.times.s(Inh,high),
t_2(Inh)=m(Inh,low)+3.times.s(Inh,low),
t_3(NI)=m(NI,low)-3.times.s(NI,low),
t_4(NI)=m(NI,low)+3.times.s(NI,low),
t_5(NI)=m(NI,high)-3.times.s(NI,high),
t_6(NI)=m(NI,high)+3.times.s(NI,high),
An auto-fluorescent sample was identified if its read-out, NI or
Inh, obeyed one of the following conditions; Inh<t_1(Inh),
Inh>t_2(Inh), NI<a.sub.1+b.sub.1.times.Inh, or
NI>a.sub.2+b.sub.2.times.Inh, with
a.sub.1=t_3(NI)-b.sub.1.times.m(Inh,low),
a.sub.2=t_4(NI)-b.sub.2.times.m(Inh,low),
b.sub.1=[t_3(NI)-t_5(NI)]/[m(Inh,low)-m(Inh,high)], and
b.sub.2=[t_4(NI)-t_6(NI)]/[m(Inh,low)-m(Inh,high)]. In this way,
all 45 samples with added auto-fluorescence were identified.
FIG. 2B plots the two-dimensional distribution of
chi.sup.2-inhibition pairs, (chi.sup.2, Inh), from both sets of 45
samples (black dots), the high samples (gray cross), and the low
samples (gray circles) together with the threshold functions (black
lines) for said identification. The thresholds were set by the
mean, m(chi.sup.2,high)=1.37, m(Inh,high)=0, m(chi.sup.2,low)=0.71,
and m(Inh,low)=100, and the standard deviation,
s(chi.sup.2,high)=0.24, s(Inh,high)=5.8, s(chi.sup.2,low)=0.06, and
s(Inh,low)=2.7, of chi.sup.2 and Inh from all ten high and low
samples, respectively; t_1(Inh)=m(Inh,high)-3.times.s(Inh,high),
t_2(Inh)=m(Inh,low)+3.times.s(Inh,low),
t_3(chi.sup.2)=m(chi.sup.2,low)-5.times.s(chi.sup.2,low),
t_4(chi.sup.2)=m(chi.sup.2,low)+5.times.s(chi.sup.2,low),
t_5(chi.sup.2)=m(chi.sup.2,high)-5.times.s(chi.sup.2,high),
t_6(chi.sup.2)=m(chi.sup.2,high)+5.times.s(chi.sup.2,high), An
auto-fluorescent sample was identified if its read-out, chi.sup.2
or Inh, obeyed one of the following conditions; Inh<t_1(Inh),
Inh>t_2(Inh), chi.sup.2<a.sub.1+b.sub.1.times.Inh, or
chi.sup.2>a.sub.2+b.sub.2.times.Inh, with
a.sub.1=t_3(chi.sup.2)-b.sub.1.times.m(Inh,low),
a.sub.2=t_4(chi.sup.2)-b.sub.2.times.m(Inh,low),
b.sub.1=[t_3(chi.sup.2)-t_5(chi.sup.2)]/[m(Inh,low)-m(Inh,high)],
and
b.sub.2=[t_4(chi.sup.2)-t_6(chi.sup.2)]/[m(Inh,low)-m(Inh,high)].
Once again, all 45 samples with added auto-fluorescence were
identified.
FIG. 2C shows the titration curves for the pure samples (black
dots) and the samples with added auto-fluorescence (transparent
dots), i.e. the curve shows the change of the polarization, P, with
increasingly added unlabeled peptide (the error bars were obtained
from the results of the five samples observed for each titration
point). The effect of the auto-fluorescence on the detected
fluorescence becomes evident by a decreased polarization value.
Fitting the auto-fluorescent samples with an additional pair of
floating FIDA-background values resulted in a correction of the
read-out. This is demonstrated in FIG. 2D, where the corrected
read-out of the auto-fluorescent samples coincides with the
read-out of the pure samples.
FIG. 2E demonstrates the test procedure of the correction step.
Similar to FIG. 2B, it plots the two-dimensional distribution of
corrected chi.sup.2-inhibition pairs, (chi.sup.2, Inh), from both
sets of 45 samples (black dots), the low samples (gray cross), and
the high samples (gray circles) together with the threshold
functions (black lines) for said identification. The thresholds
were identically set as in FIG. 2B.
The failure of the correction step was identified if the according
read-out, chi.sup.2 or Inh, obeyed one of the following conditions;
Inh>t_2(Inh), chi.sup.2<a.sub.1+b.sub.1.times.Inh, or
chi.sup.2>a.sub.2+b.sub.2.times.Inh, with
a.sub.1=t_3(chi.sup.2)-b.sub.1.times.m(Inh,low),
a.sub.2=t_4(chi.sup.2)-b.sub.2.times.m(Inh,low),
b.sub.1=[t_3(chi.sup.2)-t_5(chi.sup.2)]/[m(Inh,low)-m(Inh,high)],
and
b.sub.2=[t_4(chi.sup.2)-t_6(chi.sup.2)]/[m(Inh,low)-m(Inh,high)].
In this way, only one failure of the correction step was
identified.
Example 3
In a third measurement series, the binding of a TAMRA-labeled
ligand to membrane vesicles with the appropriate G-protein coupled
receptors was monitored using FIDA (one-detector set-up,
measurement time of two seconds). The ligand bound to the vesicles
can be distinguished from the free ligand by an increase in the
fluorescence brightness, q, since the vesicles can bind several
ligands. In FIDA, these two components were distinguished in a
two-component fit and their brightness, q(ligand) and q(vesicle),
and concentration values, c(ligand) and c(vesicle), were
determined. For every sample the binding degree was determined
according to the equation, binding
degree=c(vesicle).times.q(vesicle)/[c(vesicle).times.q(vesicle)+c(ligand)-
.times.q(ligand)].
48 high control and 48 low control sample were measured. The high
control contained both, labeled ligand and vesicles, while the low
control solely contained labeled ligand. In a first set of
measurements, the pure 96 samples were observed. In additional sets
of measurement, the 96 samples were observed after adding different
amounts of auto-fluorescent substances (0.05 .mu.M, 0.5 .mu.M, and
1 .mu.M of the dye C682). The binding degree resulting from the
two-component FIDA fit is shown in FIG. 3A. The apparently
decreased binding degree shows the effect of the increasingly added
auto-fluorescent substances.
For the correction, a three-component FIDA analysis was performed
on the same fluorescence data sets. Thereby, an additional
component with floating concentration, c(auto-fluorescence), and
fixed brightness value, q(auto-fluorescence)=1 kHz, was added to
the two-component fit of FIG. 3A. The fixed brightness value was
rather low compared to the mean brightness values obtained for the
free ligand, q(ligand)=9 kHz, and the ligand bound to the vesicle,
q(vesicle)=1350 kHz. The resulting values of the binding degree
coincides with that of the pure samples, which indicates the
success of the correction procedure. However, a decreased accuracy
of the determination of the binding degree becomes evident by the
increased error bars, which is due to the presence of the
interfering auto-fluorescence as well as the correction procedure.
Therefore, it is recommendable to apply this correction step solely
to those samples which are identified to emit
auto-fluorescence.
Example 4
In a fourth measurement series, 96 different compounds were tested
for the activation of a DNA-binding protein. Upon activation, the
protein was able to bind the single DNA strand. Since the DNA
strand was labeled with TAMRA, the activation was accompanied by an
increase in the polarization, P. A positive compound, which
activated the protein, should therefore result in an increase of
polarization, P. To check the reactivity of the compounds, the
polarization read-out was compared to that of positive and negative
controls. While the negative control just like a non-activating
compound comprised the unbound DNA strand (low polarization), the
positive control just like an activating compound comprised the
DNA-peptide complex (high polarization). As in the previous example
2, the measurements were performed with two detectors monitoring
the different polarization directions of the light emission and
analyzed using 2D-FIDA regarding only one fluorescent component.
This resulted in values of the intensity, I.sub.P and I.sub.S, as
well as of the brightness, q.sub.1 and q.sub.2, of the fluorescence
with parallel and perpendicular polarization with respect to the
polarization of the exciting light, respectively, and of the mean
concentration, c, of the fluorescent component. This enabled the
calculation of the total intensity, I.sub.tot, the total
brightness, q.sub.tot, the activation, Act, as well as the
normalized total signal, NI.
I.sub.tot=I.sub.P+2I.sub.Sq.sub.tot=q.sub.1+q.sub.2P=(q.sub.1-q.sub.2)/(q-
.sub.1+q.sub.2).times.1000
NI(X)=[I.sub.tot(X)-I.sub.tot(pos)]/[I.sub.tot(neg)-I.sub.tot(pos)].times-
.100 Act(X)=[P(X)-P(neg)]/[P(pos)-P(neg)].times.100
The whole experiment included the measurement (two second duration)
of 96 different compounds added to the assay (labeled DNA and
protein) as well as nine positive controls and 96 negative
controls.
For the identification of possible auto-fluorescent or quenching
compounds, FIG. 4A plots the two-dimensional distribution of joint
normalized total intensity-activation pairs, (NI, Act), from the 96
compound samples (black dots), the 96 negative control samples
(gray cross), and the six positive control samples (gray circles)
together with the threshold functions (black lines) for said
identification. In the same way as in FIG. 2A, the thresholds were
set by the mean, m(NI,pos)=0, m(Act,pos)=100, m(NI,neg)=100, and
m(Act,neg)=0, and the standard deviation, s(NI,pos)=4.1,
s(Act,pos)=4.7, s(NI,neg)=9.3, and s(Act,neg)=6.2, of NI and Act
from all six positive and 96 negative control samples,
respectively; t_1(Act)=m(Act,neg)-3.times.s(Act,neg),
t_2(Act)=m(Act,pos)+3.times.s(Act,pos),
t_3(NI)=m(NI,neg)-3.times.s(NI,neg),
t_4(NI)=m(NI,neg)+3.times.s(NI,neg),
t_5(NI)=m(NI,pos)-3.times.s(NI,pos),
t_6(NI)=m(NI,pos)+3.times.s(NI,pos).
An auto-fluorescent compound sample was identified if its read-out,
NI or Act, was above the upper threshold line, i.e. obeyed the
following condition; NI>a.sub.2+b.sub.2.times.Act, with
a.sub.2=t_4(NI)-b.sub.2.times.m(Act,neg), and
b.sub.2=[t_4(NI)-t_6(NI)]/[m(Act,neg)-m(Act,pos)]. In this way, 67
compound samples were identified to be auto-fluorescent.
A quenching compound sample was identified if its read-out, NI or
Act, was below the lower threshold line or elsewhere to the left or
right of the two vertical lines, i.e. obeyed one of the following
conditions and was not auto-fluorescent;
Act<t_1(Act),Act>t_2(Act), or
NI<a.sub.1+b.sub.1.times.Act, with
a.sub.1=t_3(NI)-b.sub.1.times.m(Act,neg), and,
b.sub.1=[t_3(NI)-t_5(NI)]/[m(Act,neg)-m(Act,pos)]. In this way, two
compound samples were identified to be quenching and taken away
from further analysis (bad data points).
In a second step, the correction procedure was applied to the
fluorescence data from the compound samples identified as being
auto-fluorescent (while the results of the analysis were maintained
for the valid compound samples). The correction procedure comprised
a 2D-FIDA fit regarding one component as before and in addition two
floating FIDA-background values as already applied in example 2.
The success of the correction procedure was checked (see FIG. 4B).
Similar to FIG. 2B, it plots the two-dimensional distribution of
the corrected total brightness-activation pairs, (q.sub.tot, Act),
from the 94 left samples (black dots), the 96 negative samples
(gray cross), and the six positive samples (gray circles) together
with the threshold functions (black lines) for the identification
of failures of the correction algorithm or the analysis in
principle. The thresholds were set by the mean,
m(q.sub.tot,pos)=68.0, m(Act,pos)=100, m(q.sub.tot,neg)=58.4, and
m(Act,neg)=0, and the standard deviation, s(q.sub.tot,pos)=3.7,
s(Act,pos)=4.7, s(q.sub.tot,neg)=3.5, and s(Act,neg)=6.2, of
q.sub.tot and Act from all six positive and 96 negative control
samples, respectively; t_1(Act)=m(Act,neg)-3.times.s(Act,neg),
t_2(Act)=m(Act,pos)+3.times.s(Act,pos),
t_3(q.sub.tot)=m(q.sub.tot,neg)-5.times.s(q.sub.tot,neg),
t_4(q.sub.tot)=m(q.sub.tot,neg)+5.times.s(q.sub.tot,neg),
t_5(q.sub.tot)=m(q.sub.tot,pos)-5.times.s(q.sub.tot,pos),
t_6(q.sub.tot)=m(q.sub.tot,pos)+5.times.s(q.sub.tot,pos). The said
failure was identified if the according read-out, q.sub.tot or Act,
obeyed one of the following conditions;
Act>t_2(Act),q.sub.tot<a.sub.1+b.sub.1.times.Act, or
q.sub.tot>a.sub.2+b.sub.2.times.Act, with
a.sub.1=t_3(q.sub.tot)-b.sub.1.times.m(Act,low),
a.sub.2=t_4(q.sub.tot)-b.sub.2.times.m(Act,low),
b.sub.1=[t_3(q.sub.tot)-t_5(chi.sup.2)]/[m(Act,low)-m(Act,high)],
and
b.sub.2=[t_4(q.sub.tot)-t_6(q.sub.tot)]/[m(Act,low)-m(Act,high)].
In this way, eight failures of the whole analysis process were
identified.
Using the identification step and correction procedure, together
with the failure check, one can not only exclude possible false
positive compounds (i.e., apparently activating in this case) due
to auto-fluorescence or quenching, but also correct the read-out
for auto-fluorescent compounds. In a drug discovery process, this
does not only save precious money and time, but also helps to find
possible positive and auto-fluorescent drug candidates which would
otherwise be lost.
Example 5
In a further measurement series, the identification step was
applied to a high-throughput-screening (HTS) run. In this HTS run
different compounds were tested for the inhibition of the
dephosphorylation of a phosphotyrosine-containing peptide by an
appropriate protein tyrosine phosphatase. An antibody was used in
this experiment which only binds to the phosphorylated peptide.
Since the peptide was fluorescently labeled, binding of the
antibody to the phosphorylated peptide increased the polarization,
P, of this complex. Therefore, dephosphorylation resulted in a loss
of antibody binding and concomitant decrease of polarization. A
positive compound, which inhibited the dephosphorylation, should
therefore result in an increase of polarization, P. To check the
reactivity of the compounds, the polarization read-out was compared
to that of positive and negative controls. While the negative
control just like a non-inhibiting compound comprised the
dephosphorylated peptide (low polarization), the positive control
just like an inhibiting compound comprised the antibody-peptide
complex (high polarization). As in the previous examples 2 and 4,
the measurements were performed with two detectors monitoring the
different polarization directions of the light emission and
analyzed using 2D-FIDA with a one-component fit. As outlined, this
enabled the calculation of the inhibition, Inh, as well as the
normalized total signal, NI.
6144 different compounds were added to the assay (labeled peptide,
antibody, and phosphatase) and distributed on four different
nanotiter-plates with 2080 wells each. Furthermore, each plate
contained 24 positive and 24 negative control samples. The HTS run
was performed by measuring each sample once for one second. The
identification step for auto-fluorescent or quenching compounds is
outlined in FIG. 5A. In the same way as outlined in detail in FIG.
2A and FIG. 4A, the threshold conditions for the identification
were set individually for each plate according to the mean values
and standard deviations of Inh and NI of the positive and negative
controls (mean.+-.3.times.standard deviation).
This is shown in FIG. 5A for one of the four plates, where the
threshold lines (black lines) are drawn such as in FIGS. 2A and 4A.
The compound samples exhibiting a read-out pair of (Inh,NI) above
the upper line were classified as auto-fluorescent compounds (gray
cross), while compound samples exhibiting a read-out pair of
(Inh,NI) below the lower line were classified as quenching
compounds (gray circles). Valid compound samples as well as
positive and negative controls (black circles) lie in between the
threshold lines. In this way, 1313 compounds were classified to be
valid, 166 (10.8%) to be quenching, and 57 (3.7%) to be
auto-fluorescent.
FIG. 5B plots the pairs (Inh, NI) from all four plates. The
identification step was performed for each plate independently. In
this way, 4966 valid (black circles), 819 quenching (13.3%, gray
circles), and 365 auto-fluorescent compounds (5.9%, gray cross)
were identified in this HTS run.
Since the inhibition values obtained from the samples with
auto-fluorescent and quenching compounds in a lot of cases pretend
a positive inhibiting property of the according compound (compare
FIG. 5), this identification step avoids the detection of false
positives and helps to save precious money and time in the drug
discovery process when using HTS.
Example 6
FIG. 6 shows a schematic diagram of a preferred system for
detecting the impacts of interfering effects on experimental data
resulting from fluorescence measurements. Preferably, the
fluorescence measurements are performed with a confocal
epi-illuminated microscope.
Means in an inspection station (2) support one or a plurality of
samples (e.g. a moveable microscope table with a 4.times.6-, 96-,
384-, 1536-, or 2080-well glass bottom well plate, the wells being
filled with the samples). Preferably, the samples comprise
dye-labeled molecules at a rather low concentration below 20 nM.
Furthermore, the inspection station can preferably be moved with
respect to the rest of the system.
One or a plurality of light sources (3) serve for the excitation of
fluorescence emission within the sample. Preferably, the light
sources are linearly polarized lasers at wavelengths between 350
and 700 nm, which are either continuous wave or pulsed in the case
of fluorescence lifetime measurements. In order to guide the
excitation light onto the sample, it is reflected by a mirror (4)
and focused into the sample by a lens (5). Preferably, the mirror
is dichroitic, i.e. it reflects the excitation light and transmits
the fluorescence light. Preferably, the lens is an objective lens,
which focuses the light to an almost diffraction limited spot of
about 1 .mu.m diameter, thereby causing fluorescence emission
within the sample.
For the detection of the fluorescence emission, the system
comprises an optical set-up which directs the fluorescence on at
least one of the detectors (9, 10). The fluorescence of the sample
is collected by the same lens (5), transmits the mirror (4), and is
focused onto a pinhole (6). The pinhole, which preferably has a
diameter of 10 to 200 .mu.m, blocks out-of-focus light and
transmits only fluorescence light, which is emitted within the
focal part of the excitation light, i.e. a volume of about fL-size.
After the pinhole, the fluorescence is guided to one or more
detectors (9, 10). It can be split into several components by one
or more mirrors (7), which preferably split the fluorescence into
its components of different polarization and/or color. Before
impinging onto the detectors, the fluorescence radiation can pass
optical filters (8), which preferably transmit the fluorescence and
block unwanted radiation such as scattering from the solvent.
Preferably, the detectors (9, 10) are avalanche photodiodes, which
convert the impinged fluorescence radiation into an electrical
signal with a very high efficiency.
A signal processing unit (11) converts the electrical signal of the
one or the plurality of detectors into experimental data, which is
preferably a stream of fluorescence photon counts. In further
processing steps, the unit (11) determines the values of one or a
plurality of identification parameters from the experimental data,
which is e.g. the amount of detected fluorescence--e.g. the
fluorescence intensity, the number of counts and/or the
count-rate--, a ratio of fluorescence intensities at selected
wavelengths, a ratio of fluorescence intensities at different
polarization directions, a fluorescence anisotropy, a fluorescence
polarization, a fluorescence lifetime, a rotational correlation
time, a diffusion constant, a concentration of fluorophores, a
specific fluorescence brightness, and/or a function of these. For
this determination, the signal processing unit uses preferably
analysis techniques such as FCS, 1D- and/or 2D-FIDA, FILDA,
fluorescence lifetime and/or time-resolves anisotropy analysis,
and/or FIMDA. Furthermore, the signal processing unit (11) might
coordinate the movement of the sample support within the inspection
station. The signal processing unit preferably contains a storage
unit, which stores the determined values of identification
parameters in relation to the respective position of the sample
support. The signal processing unit (11) as well creates an
histogram or distribution of the values of the identification
parameters and determines thresholds for the values of the
identification parameters, which thresholds are indicative for the
impact of interfering effects. It analyzes the values of the
identification parameters for the different positions of the sample
support within the inspection station and determines whether or not
these values fulfill criteria with respect to the thresholds. It
also supplies as output information those data which are influenced
and/or not influenced by the interfering effects. Furthermore, the
unit (11) includes means for correcting the data for the impact of
the interfering effect and means for the conductance of a control
step to check the success of the correction.
* * * * *